Management of patients with chronic disease consumes a significant proportion of the total health care expenditure in the United States. Many of these diseases, such as heart disease, are widely prevalent and have significant annual incidences as well. Patients with chronic heart disease can receive implanted cardiac rhythm management (CRM) devices such as pacemakers, implantable cardioverter defibrillators (ICDs), and heart failure cardiac resynchronization therapy (CRT) devices to provide treatment for the disease.
Advanced patient management (APM) systems allow caregivers to remotely gather and analyze data associated with a patient and the patient's CRM device. APM systems provide a vast amount of information to the caregiver in an automated manner. This information can provide insights into a patient's well being and help the caregiver predict significant changes in a patient's health, such as a decompensation event associated with a heart attack. However, the time lag between when data is updated on a CRM device and when it is collected, analyzed, and presented for review by the APM system can reduce the timeliness of the information provided to the caregiver.
For example, CRM devices can update device data stored in the CRM device memory at periodic intervals, such as once per day. One example of device data that can be updated periodically by a CRM device is heart rate variability. For example, the CRM device can be programmed to update an average heart rate variability for a patient once per day. The timing for these device updates is usually arbitrarily set at the time at which the CRM device is originally initiated prior to or at the time of implantation. There can be a significant time lag due to a lack of coordination between the device data update time by a CRM device and the time at which an APM system collects data from (e.g., interrogates) the device.
For example, a CRM device can be arbitrarily set to update device data at time-of-day A in day 1, as shown in FIG. 1. Assume that the APM system interrogates the CRM device at time-of-day B in day 2, and that the caregiver accesses the APM system to review the information that the APM system has collected from the device and analyzed at time-of-day C. Although the entire interval or lag D between device data (time-of-day A) and caregiver review (time-of-day C) spans two days, it is a relatively short period, so that the caregiver is reviewing recently acquired and analyzed information.
However, in another example shown in FIG. 2, assume again that the CRM device is arbitrarily set to update device data at time-of-day A earlier in day 1, and that the APM system interrogates the CRM device at time-of-day B in day 2. Also assume that the caregiver does not review the information on the APM system until later at time-of-day C. In this scenario, lag D is more significant, resulting in less-timely information being provided to the caregiver. In a worst-case scenario based on daily device updates and interrogations, the caregiver could be presented with information that is forty-eight (48) hours old. It is desirable to minimize lag D so that the caregiver is given data that is as current as possible so that the caregiver can make timely decisions regarding a patient's health.
In addition to the potential time lag problems associated with the collection of data, an APM system can potentially be used to analyze data associated with thousands or millions of patients at any given time. It is therefore desirable to optimize analysis of data on the APM system such that the APM system can efficiently analyze each patient's data while presenting current data to each caregiver as the caregiver accesses the APM system.